Integrating new technologies in university second language instruction : teachers' perspectives
Bibliographic record
Abstract
This study explores the relationships among technology, language literacy and instruction in University Continuing Education Institutions. Adult second language education strives to update media resources in an information era in which literacy encompasses the abilities to communicate both in different languages and in a variety of media across disciplines. The main assumption underlying this study is that teaching adults to use language in an era in which networks and multimedia are major components, is a challenging task and responsibility. Instructional implications of literature regarding the implementation of new technologies in language learning suggest a persistent disagreement on the merits of new technologies as learning tools and a mismatch between expectations and applications of new media. I interview four teachers of second or foreign languages to adults to explore the challenges embedded in mediating adults' literacy in using multiple representations of second language knowledge within technology enhanced classroom environments. Teachers are shown to integrate digital technologies into traditional print and audiovisual tools to advance three main literacies: Cultural literacy relates to the ability to make socioculturally appropriate links of language and media. Disciplinary literacy denotes the ability to effectively identify, analyze, evaluate and apply language resources in various contexts. Media literacy denotes the ability to make informed choices among the various language representations. Integrated media applications are challenging for teachers who need to be aware of media benefits and constraints. The ongoing development of teachers' media literacy is a prerequisite for meaningful and constructive uses of the instructional resources available that will enable adults to apply second language knowledge within and beyond linguistic, cultural, and disciplinary contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".